In [33]:
print("Upregulated in contrast "+contrast)
sc.pl.violin(adata,
keys = UP[:4] ,
groupby=groupingCovariate)
Upregulated in contrast proximal_vs_distal
import scanpy as sc, anndata as ad, numpy as np, pandas as pd
from scipy import sparse
from anndata import AnnData
import warnings
import socket
import plotly.express as px
from matplotlib import pylab
import sys
import yaml
import os
import matplotlib.pyplot as plt
import scvelo as scv
from pybiomart import Server
import rpy2
from pandas.api.types import CategoricalDtype
import rpy2.robjects as ro
import seaborn as sns
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
import plotly.graph_objects as go
import ipyparams
warnings.filterwarnings('ignore')
outdir="./outdir"
FinaLeaf = "/Progenitors"
output_basename = "09B_Progenitors_pBulk_bySegment.DEA"
mappingDict = {}
categoriesOrder = ["piece1","piece2","piece3","distal","medial","proximal"]
groupingCovariate = "group"
PseudooReplicates_per_group = 10
markers = "./data/resources/F_T.markers.scored.tsv"
totalPath = outdir+FinaLeaf+"/5B_Progenitors_pBulk.bySegment."+str(PseudooReplicates_per_group)+"PRs.tsv"
adataPath = outdir+FinaLeaf+"/5B_Progenitors_pBulk.bySegment."+str(PseudooReplicates_per_group)+"PRs.h5ad"
badhuriMarkers = ["PAX6","DDIT3","NEUROG2","CNBP","HMGB2","ZNF707","CAMTA1","SUB1","THYN1","NEUROG1","HMGB3","NFIX","BCL11A","TCF4","NR2F1"]
%load_ext rpy2.ipython
%%R
library(edgeR)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(stats)
library(topGO)
R[write to console]: Loading required package: limma
R[write to console]: Loading required package: AnnotationDbi
R[write to console]: Loading required package: stats4
R[write to console]: Loading required package: BiocGenerics
R[write to console]: Loading required package: parallel
R[write to console]:
Attaching package: ‘BiocGenerics’
R[write to console]: The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
R[write to console]: The following object is masked from ‘package:limma’:
plotMA
R[write to console]: The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
R[write to console]: The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
R[write to console]: Loading required package: Biobase
R[write to console]: Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
R[write to console]: Loading required package: IRanges
R[write to console]: Loading required package: S4Vectors
R[write to console]:
Attaching package: ‘S4Vectors’
R[write to console]: The following object is masked from ‘package:base’:
expand.grid
R[write to console]:
R[write to console]: Loading required package: graph
R[write to console]: Loading required package: GO.db
R[write to console]:
R[write to console]: Loading required package: SparseM
R[write to console]:
Attaching package: ‘SparseM’
R[write to console]: The following object is masked from ‘package:base’:
backsolve
R[write to console]:
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
R[write to console]:
Attaching package: ‘topGO’
R[write to console]: The following object is masked from ‘package:IRanges’:
members
%matplotlib inline
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=50, facecolor='white')
pylab.rcParams['figure.figsize'] = (10, 10)
scanpy==1.8.1 anndata==0.7.6 umap==0.4.6 numpy==1.20.2 scipy==1.6.3 pandas==1.2.4 scikit-learn==0.24.2 statsmodels==0.13.1 python-igraph==0.9.8 louvain==0.7.1 pynndescent==0.5.5
hostRoot = "-".join(socket.gethostname().split('-')[0:2])
with open(os.path.expanduser('~')+"/paths_config.yaml", 'r') as f:
paths = yaml.load(f, Loader=yaml.FullLoader)
#indir=paths["paths"]["indir"][hostRoot]
#projectBaseDir=paths["paths"]["projectBaseDir"][hostRoot]
adata = sc.read_h5ad(adataPath)
adata.obs[groupingCovariate] = adata.obs[groupingCovariate].astype(CategoricalDtype(categories=categoriesOrder, ordered=True))
total = pd.read_csv(totalPath, sep="\t", index_col=0)
#adata = adata[adata.obs["group"].isin(RelevantAreas)]
total = total[adata.obs_names]
edgeR_topTags = ro.r['topTags']
edgeR_glmLRT = ro.r['glmLRT']
edgeR_glmQLFTest = ro.r['glmQLFTest']
as_data_frame = ro.r['as.data.frame']
rprint = rpy2.robjects.globalenv.find("print")
DEGs = {}
TestCov = groupingCovariate
adata.obs.group.value_counts()
piece1 10 piece2 10 piece3 10 distal 10 medial 10 proximal 10 Name: group, dtype: int64
obs = adata.obs[[TestCov,"pseudoreplicate"]].copy()
obs["pseudoreplicate"] = obs.index.tolist()
obs["TestCov"] = obs[TestCov].astype(str)
#obs["TestCov"] = obs[TestCov]
totalRelevant = total[obs.index]
totalRelevant.head()
| distal_0 | distal_1 | distal_2 | distal_3 | distal_4 | distal_5 | distal_6 | distal_7 | distal_8 | distal_9 | ... | proximal_0 | proximal_1 | proximal_2 | proximal_3 | proximal_4 | proximal_5 | proximal_6 | proximal_7 | proximal_8 | proximal_9 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MIR1302-2HG | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 1.006109 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| FAM138A | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| OR4F5 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| AL627309.1 | 0.0 | 0.958561 | 0.0 | 0.673234 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 3.919002 | 1.317389 | 0.830392 | 1.786431 | 0.0 | 1.688701 | 2.579079 | 6.112021 | 2.980404 | 2.187134 |
| AL627309.3 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
5 rows × 60 columns
minCounts = totalRelevant[totalRelevant > 0].min(axis = 1).min()
#Universe 1 count in at least 5% of samples per group
groupThreshold = round(pd.get_dummies(obs[TestCov]).T.sum(axis = 1) *0.05)
bolMatrix = (np.matrix(np.dot(pd.get_dummies(obs[TestCov]).T, (totalRelevant > 0).astype(int).T)) - np.matrix(groupThreshold).T > 0)
bolVect = (bolMatrix.sum(axis = 0) >= 1).A1
totalRelevant = totalRelevant.loc[bolVect]
len(totalRelevant)
25417
universe = totalRelevant.index.tolist()
levelsToMap = adata.obs[groupingCovariate].cat.categories.tolist()
levelsToMap
['piece1', 'piece2', 'piece3', 'distal', 'medial', 'proximal']
%%R -i obs -i totalRelevant -i levelsToMap -o dds
mmVector<- factor(obs$TestCov, levels = c(levelsToMap))
mm <- model.matrix(~mmVector )
row.names(mm) <- colnames(totalRelevant)
dds <- DGEList(totalRelevant, group = obs$TestCov, genes = rownames(totalRelevant))
#Ennotate with entrez genes
anno <- select(org.Hs.eg.db, keys=rownames(dds$counts),columns=c("ENTREZID","SYMBOL"),keytype="SYMBOL")
colnames(anno) <- c("genes","entrez")
anno <- anno[!duplicated(anno$genes, fromLast=T), ]
dds$genes$entrez <- merge(x = data.frame(dds$genes), y = anno, by = "genes", all.x = TRUE)$entrez
dim(dds)
R[write to console]: 'select()' returned 1:many mapping between keys and columns
[1] 25417 60
%%R
mm
(Intercept) mmVectorpiece2 mmVectorpiece3 mmVectordistal
distal_0 1 0 0 1
distal_1 1 0 0 1
distal_2 1 0 0 1
distal_3 1 0 0 1
distal_4 1 0 0 1
distal_5 1 0 0 1
distal_6 1 0 0 1
distal_7 1 0 0 1
distal_8 1 0 0 1
distal_9 1 0 0 1
medial_0 1 0 0 0
medial_1 1 0 0 0
medial_2 1 0 0 0
medial_3 1 0 0 0
medial_4 1 0 0 0
medial_5 1 0 0 0
medial_6 1 0 0 0
medial_7 1 0 0 0
medial_8 1 0 0 0
medial_9 1 0 0 0
piece1_0 1 0 0 0
piece1_1 1 0 0 0
piece1_2 1 0 0 0
piece1_3 1 0 0 0
piece1_4 1 0 0 0
piece1_5 1 0 0 0
piece1_6 1 0 0 0
piece1_7 1 0 0 0
piece1_8 1 0 0 0
piece1_9 1 0 0 0
piece2_0 1 1 0 0
piece2_1 1 1 0 0
piece2_2 1 1 0 0
piece2_3 1 1 0 0
piece2_4 1 1 0 0
piece2_5 1 1 0 0
piece2_6 1 1 0 0
piece2_7 1 1 0 0
piece2_8 1 1 0 0
piece2_9 1 1 0 0
piece3_0 1 0 1 0
piece3_1 1 0 1 0
piece3_2 1 0 1 0
piece3_3 1 0 1 0
piece3_4 1 0 1 0
piece3_5 1 0 1 0
piece3_6 1 0 1 0
piece3_7 1 0 1 0
piece3_8 1 0 1 0
piece3_9 1 0 1 0
proximal_0 1 0 0 0
proximal_1 1 0 0 0
proximal_2 1 0 0 0
proximal_3 1 0 0 0
proximal_4 1 0 0 0
proximal_5 1 0 0 0
proximal_6 1 0 0 0
proximal_7 1 0 0 0
proximal_8 1 0 0 0
proximal_9 1 0 0 0
mmVectormedial mmVectorproximal
distal_0 0 0
distal_1 0 0
distal_2 0 0
distal_3 0 0
distal_4 0 0
distal_5 0 0
distal_6 0 0
distal_7 0 0
distal_8 0 0
distal_9 0 0
medial_0 1 0
medial_1 1 0
medial_2 1 0
medial_3 1 0
medial_4 1 0
medial_5 1 0
medial_6 1 0
medial_7 1 0
medial_8 1 0
medial_9 1 0
piece1_0 0 0
piece1_1 0 0
piece1_2 0 0
piece1_3 0 0
piece1_4 0 0
piece1_5 0 0
piece1_6 0 0
piece1_7 0 0
piece1_8 0 0
piece1_9 0 0
piece2_0 0 0
piece2_1 0 0
piece2_2 0 0
piece2_3 0 0
piece2_4 0 0
piece2_5 0 0
piece2_6 0 0
piece2_7 0 0
piece2_8 0 0
piece2_9 0 0
piece3_0 0 0
piece3_1 0 0
piece3_2 0 0
piece3_3 0 0
piece3_4 0 0
piece3_5 0 0
piece3_6 0 0
piece3_7 0 0
piece3_8 0 0
piece3_9 0 0
proximal_0 0 1
proximal_1 0 1
proximal_2 0 1
proximal_3 0 1
proximal_4 0 1
proximal_5 0 1
proximal_6 0 1
proximal_7 0 1
proximal_8 0 1
proximal_9 0 1
attr(,"assign")
[1] 0 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$mmVector
[1] "contr.treatment"
%%R -i minCounts
#keep <- filterByExpr(dds, min.count = minCounts, min.total.count = minCounts*5)
#dds <- dds[keep,,keep.lib.sizes=FALSE]
dim(dds)
[1] 25417 60
%%R
dds <- calcNormFactors(dds)
dds <- estimateGLMRobustDisp(dds, mm)
fit <- glmFit(dds, mm)
levelsToMap
['piece1', 'piece2', 'piece3', 'distal', 'medial', 'proximal']
%%R -o piece2_vs_piece1 -o piece3_vs_piece1 -o distal_vs_piece1 -o medial_vs_piece1 -o proximal_vs_piece1 -o proximal_vs_distal -o medial_vs_distal
#-o Mid_vs_early -o Late_vs_mid
topn=10000
piece2_vs_piece1 = topTags(glmLRT(fit, coef=2), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
piece3_vs_piece1 = topTags(glmLRT(fit, coef=3), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
distal_vs_piece1 = topTags(glmLRT(fit, coef=4), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
medial_vs_piece1 = topTags(glmLRT(fit, coef=5), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
proximal_vs_piece1 = topTags(glmLRT(fit, coef=6), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
proximal_vs_distal = topTags(glmLRT(fit, contrast=c(0,0,0,-1,0,1)), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
medial_vs_distal = topTags(glmLRT(fit, contrast=c(0,0,0,-1,1,0)), adjust.method = "bonferroni", n = topn, sort.by = "logFC")
DEGsDict = {"piece2_vs_piece1":piece2_vs_piece1,
"piece3_vs_piece1":piece3_vs_piece1,
"distal_vs_piece1":distal_vs_piece1,
"medial_vs_piece1":medial_vs_piece1,
"proximal_vs_piece1":proximal_vs_piece1,
"proximal_vs_distal":proximal_vs_distal,
"medial_vs_distal":medial_vs_distal,
}
markersDF = pd.read_csv(markers, header=None, sep = "\t", names=["name","area","score"])
temporalMarkers = markersDF.loc[markersDF["area"] == "Temporal","name"]
frontalMarkers = markersDF.loc[markersDF["area"] == "Frontal","name"]
LOGCFTHRESHOLD = 1.5
for k in list(DEGsDict.keys()):
DEGsDict[k] = rpy2.robjects.pandas2ri.rpy2py(as_data_frame(DEGsDict[k]))
DEGsDict[k]["-logPVal"] = -np.log10(DEGsDict[k].PValue)
DEGsDict[k]["significant"] = "notSignificant"
DEGsDict[k].loc[(DEGsDict[k]["logFC"] < -LOGCFTHRESHOLD) & (DEGsDict[k]["FWER"] < 0.01),"significant"] = "Down"
DEGsDict[k].loc[(DEGsDict[k]["logFC"] > LOGCFTHRESHOLD) & (DEGsDict[k]["FWER"] < 0.01),"significant"] = "Up"
#DEGsDict[k]["knownMarker"] = "unlisted"
#DEGsDict[k].loc[set(frontalMarkers).intersection(DEGsDict[k].index),"knownMarker"] = "frontal"
#DEGsDict[k].loc[set(temporalMarkers).intersection(DEGsDict[k].index),"knownMarker"] = "temporal"
color_discrete_map = {'notSignificant': 'white', 'Up': 'red', 'Down': 'blue'}
fig = px.scatter(DEGsDict[k], x="logFC", y="-logPVal",
#hover_data=DEGsDict[k][["genes","knownMarker"]],
hover_data=DEGsDict[k][["genes"]],
width=600, height=600,
color = "significant", color_discrete_map=color_discrete_map,
#symbol="knownMarker",
title=k,
template="simple_white")
fig.update_traces(mode='markers', marker_line_width=1, marker_size=10)
fig.add_hline(y=DEGsDict[k].loc[DEGsDict[k]["FWER"] - 0.01 < 0,"-logPVal" ].min(),
line_color="black",line_width=2, line_dash="dash")
fig.add_vline(x=LOGCFTHRESHOLD,
line_color="black",line_width=2, line_dash="dash")
fig.add_vline(x=-LOGCFTHRESHOLD,
line_color="black",line_width=2, line_dash="dash")
fig.show()
contrast = "proximal_vs_distal"
GOname = "GO_proximal_vs_distal.tsv"
GOlist = DEGsDict[contrast][DEGsDict[contrast]["significant"] != "notSignificant"].index.tolist()
%%R -i GOlist -i universe -o results
Universe <- universe
Temporal_vs_PFCVector <- factor(as.integer(Universe%in%GOlist))
names(Temporal_vs_PFCVector) <- Universe
BPann <- inverseList(topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(Temporal_vs_PFCVector),
mapping="org.Hs.eg.db", ID="symbol"))
GOdata <- new('topGOdata', ontology="BP", allGenes=Temporal_vs_PFCVector, annotat=annFUN.gene2GO,
gene2GO=BPann, nodeSize=15)
GOtest <- runTest(GOdata, algorithm='weight01', statistic='fisher')
results <- topGO::GenTable(GOdata, Statistics=GOtest, topNodes=length(GOtest@score), numChar=100)
R[write to console]: Building most specific GOs ..... R[write to console]: ( 11918 GO terms found. ) R[write to console]: Build GO DAG topology .......... R[write to console]: ( 15738 GO terms and 36637 relations. ) R[write to console]: Annotating nodes ............... R[write to console]: ( 14770 genes annotated to the GO terms. ) R[write to console]: -- Weight01 Algorithm -- the algorithm is scoring 3560 nontrivial nodes parameters: test statistic: fisher R[write to console]: Level 19: 1 nodes to be scored (0 eliminated genes) R[write to console]: Level 18: 1 nodes to be scored (0 eliminated genes) R[write to console]: Level 17: 5 nodes to be scored (20 eliminated genes) R[write to console]: Level 16: 10 nodes to be scored (27 eliminated genes) R[write to console]: Level 15: 21 nodes to be scored (103 eliminated genes) R[write to console]: Level 14: 38 nodes to be scored (241 eliminated genes) R[write to console]: Level 13: 71 nodes to be scored (608 eliminated genes) R[write to console]: Level 12: 125 nodes to be scored (1489 eliminated genes) R[write to console]: Level 11: 205 nodes to be scored (3524 eliminated genes) R[write to console]: Level 10: 355 nodes to be scored (5413 eliminated genes) R[write to console]: Level 9: 467 nodes to be scored (7352 eliminated genes) R[write to console]: Level 8: 524 nodes to be scored (9243 eliminated genes) R[write to console]: Level 7: 586 nodes to be scored (11166 eliminated genes) R[write to console]: Level 6: 509 nodes to be scored (12787 eliminated genes) R[write to console]: Level 5: 343 nodes to be scored (13628 eliminated genes) R[write to console]: Level 4: 189 nodes to be scored (14186 eliminated genes) R[write to console]: Level 3: 89 nodes to be scored (14416 eliminated genes) R[write to console]: Level 2: 20 nodes to be scored (14517 eliminated genes) R[write to console]: Level 1: 1 nodes to be scored (14603 eliminated genes)
FilteredResults = results[results["Statistics"].astype(float) < 0.01]
FilteredResults.to_csv(outdir+FinaLeaf+"/"+output_basename+"."+GOname, sep = "\t")
FilteredResults.Term.tolist()[:20]
['forebrain regionalization', 'cerebral cortex development', 'negative regulation of ERK1 and ERK2 cascade', 'neuron migration', 'innervation', 'forebrain neuron development', 'positive regulation of neuron differentiation', 'negative regulation of transcription by RNA polymerase II', 'axon guidance', 'pulmonary valve morphogenesis', 'negative regulation of neuron differentiation', 'positive regulation of myoblast differentiation', 'anterior/posterior pattern specification', 'negative regulation of fibroblast growth factor receptor signaling pathway', 'hippocampus development', 'negative regulation of smooth muscle cell migration', 'cell fate determination', 'endothelial cell differentiation', 'regulation of cell growth', 'heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules']
UP = DEGsDict[contrast][DEGsDict[contrast]["significant"] != "notSignificant"].sort_values("logFC", ascending = False)
UP = UP[UP.logFC > 0].index.tolist()
DOWN = DEGsDict[contrast][DEGsDict[contrast]["significant"] != "notSignificant"].sort_values("logFC", ascending = True)
DOWN = DOWN[DOWN.logFC < 0].index.tolist()
print("Upregulated in contrast "+contrast)
sc.pl.violin(adata,
keys = UP[:4] ,
groupby=groupingCovariate)
Upregulated in contrast proximal_vs_distal
print("Downregolated in contrast "+contrast)
sc.pl.violin(adata,
keys = DOWN[:4] ,
groupby=groupingCovariate)
Downregolated in contrast proximal_vs_distal
for k in DEGsDict.keys():
DEGsDict[k][DEGsDict[k].significant != "notSignificant" ].sort_values("logFC", ascending = False).to_csv(outdir+FinaLeaf+"/"+output_basename+"."+ k + ".tsv", sep = "\t")
DEGsDict[k][DEGsDict[k].significant != "notSignificant" ].sort_values("logFC", ascending = False).to_excel(outdir+FinaLeaf+"/"+output_basename+"."+ k + ".xls")
PolaroidDEGs = DEGsDict[contrast][DEGsDict[contrast]["significant"] != "notSignificant"]
PolaroidDEGs
| genes | entrez | logFC | logCPM | LR | PValue | FWER | -logPVal | significant | |
|---|---|---|---|---|---|---|---|---|---|
| p2a.dTom.rbGlob | p2a.dTom.rbGlob | 7791 | 8.075772 | 6.243359 | 349.935460 | 4.377177e-78 | 1.112547e-73 | 77.358806 | Up |
| FGF8b.WPRE.SV40 | FGF8b.WPRE.SV40 | 23140 | 7.622885 | 5.750889 | 131.252489 | 2.180331e-30 | 5.541747e-26 | 29.661478 | Up |
| IGFBP3 | IGFBP3 | 28991 | 7.169743 | 5.455421 | 160.860285 | 7.339888e-37 | 1.865579e-32 | 36.134311 | Up |
| ARHGAP20 | ARHGAP20 | 8844 | 6.344344 | 4.569972 | 63.409939 | 1.678689e-15 | 4.266724e-11 | 14.775030 | Up |
| RYR3 | RYR3 | 5125 | 6.318444 | 5.762549 | 200.705325 | 1.465263e-45 | 3.724259e-41 | 44.834084 | Up |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| JAG1 | JAG1 | 9144 | 1.519297 | 6.412512 | 65.609705 | 5.496742e-16 | 1.397107e-11 | 15.259895 | Up |
| MAML2 | MAML2 | 3820 | 1.519032 | 7.956134 | 110.947593 | 6.075576e-26 | 1.544229e-21 | 25.216413 | Up |
| LRIG1 | LRIG1 | None | 1.517736 | 7.237127 | 65.612434 | 5.489134e-16 | 1.395173e-11 | 15.260496 | Up |
| SPRED1 | SPRED1 | 5144 | 1.502520 | 8.081083 | 164.585230 | 1.126980e-37 | 2.864445e-33 | 36.948084 | Up |
| AL022068.1 | AL022068.1 | 127003 | -1.500004 | 8.168694 | 38.745854 | 4.827344e-10 | 1.226966e-05 | 9.316292 | Down |
346 rows × 9 columns
CuratedList = pd.read_csv(markers, header=None, sep = "\t", names=["name","area","score"])
CuratedList.index = CuratedList["name"]
CuratedList
| name | area | score | |
|---|---|---|---|
| name | |||
| NR2F1 | NR2F1 | Temporal | 3.0 |
| FLJ42709 | FLJ42709 | Temporal | 1.0 |
| CDS1 | CDS1 | Temporal | 1.0 |
| OPRL1 | OPRL1 | Temporal | 1.0 |
| SLC27A2 | SLC27A2 | Temporal | 1.0 |
| ... | ... | ... | ... |
| FAHD1 | FAHD1 | Frontal | 1.0 |
| CDH8 | CDH8 | Frontal | 1.0 |
| SATB2 | SATB2 | Frontal | 2.0 |
| FABP7 | FABP7 | Frontal | 1.0 |
| GRIK2 | GRIK2 | Frontal | 1.0 |
290 rows × 3 columns
CuratedListTemporal = set(CuratedList.loc[CuratedList.area == "Temporal","name"])
CuratedListFrontal = set(CuratedList.loc[CuratedList.area == "Frontal","name"])
ContrastUP = set(UP)
A = len(CuratedListTemporal.difference(CuratedListFrontal).difference(ContrastUP))
B = len(CuratedListFrontal.difference(ContrastUP).difference(CuratedListTemporal))
C = len(CuratedListTemporal.intersection(CuratedListFrontal).difference(ContrastUP))
D = len(ContrastUP.difference(CuratedListFrontal).difference(CuratedListTemporal))
E = len(ContrastUP.intersection(CuratedListTemporal).difference(CuratedListFrontal))
F = len(ContrastUP.intersection(CuratedListFrontal).difference(CuratedListTemporal))
G = len(ContrastUP.intersection(CuratedListFrontal).intersection(CuratedListTemporal))
venn3(subsets = (A, B, C, D, E, F, G), set_labels = ('CuratedListTemporal', 'CuratedListFrontal', 'ContrastUP'), alpha = 0.5);
venn3_circles(subsets = (A, B, C, D, E, F, G), linewidth=2, color='k');
print(contrast)
print("Frontal AND ContrastUP "+str(CuratedListFrontal.intersection(ContrastUP)))
print("Temporal AND ContrastUP "+str(CuratedListTemporal.intersection(ContrastUP)))
proximal_vs_distal
Frontal AND ContrastUP {'ABCA1', 'CDH6', 'TNIK', 'TENM1', 'S100A13', 'FAM184A', 'PTX3', 'ETV1', 'PRSS23', 'GREB1'}
Temporal AND ContrastUP {'NEFL'}
CuratedListTemporal = set(CuratedList.loc[CuratedList.area == "Temporal","name"])
CuratedListFrontal = set(CuratedList.loc[CuratedList.area == "Frontal","name"])
ContrastDown = set(DOWN)
A = len(CuratedListTemporal.difference(CuratedListFrontal).difference(ContrastDown))
B = len(CuratedListFrontal.difference(ContrastDown).difference(CuratedListTemporal))
C = len(CuratedListTemporal.intersection(CuratedListFrontal).difference(ContrastDown))
D = len(ContrastDown.difference(CuratedListFrontal).difference(CuratedListTemporal))
E = len(ContrastDown.intersection(CuratedListTemporal).difference(CuratedListFrontal))
F = len(ContrastDown.intersection(CuratedListFrontal).difference(CuratedListTemporal))
G = len(ContrastDown.intersection(CuratedListFrontal).intersection(CuratedListTemporal))
venn3(subsets = (A, B, C, D, E, F, G), set_labels = ('CuratedListTemporal', 'CuratedListFrontal', 'ContrastDown'), alpha = 0.5);
venn3_circles(subsets = (A, B, C, D, E, F, G), linewidth=2, color='k');
print(contrast)
print("Frontal AND ContrastDown "+str(CuratedListFrontal.intersection(ContrastDown)))
print("Temporal AND ContrastDown "+str(CuratedListTemporal.intersection(ContrastDown)))
proximal_vs_distal
Frontal AND ContrastDown {'ZIC3', 'NEUROG2'}
Temporal AND ContrastDown {'PAX6', 'RASD1', 'WNT7B', 'EPHA7', 'LHX2', 'EPHA5', 'IFI44L'}
